Token Classification
Transformers
Safetensors
English
Chinese
xlm-roberta
pii
pii-detection
de-identification
anonymization
redaction
legal
ner
Instructions to use libraos/veil-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use libraos/veil-detect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="libraos/veil-detect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("libraos/veil-detect") model = AutoModelForTokenClassification.from_pretrained("libraos/veil-detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from libraos/veil-detect: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/libraos/veil-detect/resolve/main/tokenizer.json
- Command line
-
hf download hf://libraos/veil-detect/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/libraos/veil-detect/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 516f2fc365650acf148b86e45907397a0c35af523df9e2ea3f88e25c76c1f057
- Size of remote file:
- 17.1 MB
- SHA256:
- acbd420e2269cdc1ef45332d3d5c418be4aef6b8cb5a0b7ccae0893485307153
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.